text: # Copyright 2020 Huawei Technologies Co., Ltd
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  # http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ============================================================================
  """
  #################pre_train bert example on zh-wiki########################
  python run_pretrain.py
  """
  
  import os
  import argparse
  import mindspore.communication.management as D
  from mindspore.communication.management import get_rank
  import mindspore.common.dtype as mstype
  from mindspore import context
  from mindspore.train.model import Model
  from mindspore.context import ParallelMode
  from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell
  from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor
  from mindspore.train.serialization import load_checkpoint, load_param_into_net
  from mindspore.nn.optim import Lamb, Momentum, AdamWeightDecay
  from mindspore import log as logger
  from mindspore.common import set_seed
  from src import BertNetworkWithLoss, BertTrainOneStepCell, BertTrainOneStepWithLossScaleCell, \
                  BertTrainAccumulateStepsWithLossScaleCell, BertTrainOneStepWithLossScaleCellForAdam, \
                  AdamWeightDecayForBert
  from src.dataset import create_bert_dataset
  from src.config import cfg, bert_net_cfg
  from src.utils import LossCallBack, BertLearningRate
  _current_dir = os.path.dirname(os.path.realpath(__file__))
  
  
  def _set_bert_all_reduce_split(device_target='Ascend', enable_graph_kernel=False):
      """set bert all_reduce fusion split, support num_hidden_layers is 12 and 24."""
      if bert_net_cfg.num_hidden_layers == 12:
          if bert_net_cfg.use_relative_positions:
              context.set_auto_parallel_context(all_reduce_fusion_config=[29, 58, 87, 116, 145, 174, 203, 217])
          else:
              context.set_auto_parallel_context(all_reduce_fusion_config=[28, 55, 82, 109, 136, 163, 190, 205])
              if device_target == 'GPU' and enable_graph_kernel:
                  context.set_auto_parallel_context(all_reduce_fusion_config=[180, 205])
      elif bert_net_cfg.num_hidden_layers == 24:
          if bert_net_cfg.use_relative_positions:
              context.set_auto_parallel_context(all_reduce_fusion_config=[30, 90, 150, 210, 270, 330, 390, 421])
          else:
              context.set_auto_parallel_context(all_reduce_fusion_config=[38, 93, 148, 203, 258, 313, 368, 397])
  
  
  def _get_optimizer(args_opt, network):
      """get bert optimizer, support Lamb, Momentum, AdamWeightDecay."""
      if cfg.optimizer == 'Lamb':
          lr_schedule = BertLearningRate(learning_rate=cfg.Lamb.learning_rate,
                                         end_learning_rate=cfg.Lamb.end_learning_rate,
                                         warmup_steps=cfg.Lamb.warmup_steps,
                                         decay_steps=args_opt.train_steps,
                                         power=cfg.Lamb.power)
          params = network.trainable_params()
          decay_params = list(filter(cfg.Lamb.decay_filter, params))
          other_params = list(filter(lambda x: not cfg.Lamb.decay_filter(x), params))
          group_params = [{'params': decay_params, 'weight_decay': cfg.Lamb.weight_decay},
                          {'params': other_params},
                          {'order_params': params}]
          optimizer = Lamb(group_params, learning_rate=lr_schedule, eps=cfg.Lamb.eps)
      elif cfg.optimizer == 'Momentum':
          optimizer = Momentum(network.trainable_params(), learning_rate=cfg.Momentum.learning_rate,
                               momentum=cfg.Momentum.momentum)
      elif cfg.optimizer == 'AdamWeightDecay':
          lr_schedule = BertLearningRate(learning_rate=cfg.AdamWeightDecay.learning_rate,
                                         end_learning_rate=cfg.AdamWeightDecay.end_learning_rate,
                                         warmup_steps=cfg.AdamWeightDecay.warmup_steps,
                                         decay_steps=args_opt.train_steps,
                                         power=cfg.AdamWeightDecay.power)
          params = network.trainable_params()
          decay_params = list(filter(cfg.AdamWeightDecay.decay_filter, params))
          other_params = list(filter(lambda x: not cfg.AdamWeightDecay.decay_filter(x), params))
          group_params = [{'params': decay_params, 'weight_decay': cfg.AdamWeightDecay.weight_decay},
                          {'params': other_params, 'weight_decay': 0.0},
                          {'order_params': params}]
          if args_opt.enable_lossscale == "true" and args_opt.device_target == 'GPU':
              optimizer = AdamWeightDecayForBert(group_params, learning_rate=lr_schedule, eps=cfg.AdamWeightDecay.eps)
          else:
              optimizer = AdamWeightDecay(group_params, learning_rate=lr_schedule, eps=cfg.AdamWeightDecay.eps)
      else:
          raise ValueError("Don't support optimizer {}, only support [Lamb, Momentum, AdamWeightDecay]".
                           format(cfg.optimizer))
      return optimizer
  
  
  def _auto_enable_graph_kernel(device_target, graph_kernel_mode):
      """Judge whether is suitable to enable graph kernel."""
      return graph_kernel_mode in ("auto", "true") and device_target == 'GPU' and \
          cfg.bert_network == 'base' and cfg.optimizer == 'AdamWeightDecay'
  
  
  def run_pretrain():
      """pre-train bert_clue"""
      parser = argparse.ArgumentParser(description='bert pre_training')
      parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
                          help='device where the code will be implemented. (Default: Ascend)')
      parser.add_argument("--distribute", type=str, default="false", choices=["true", "false"],
                          help="Run distribute, default is false.")
      parser.add_argument("--epoch_size", type=int, default="1", help="Epoch size, default is 1.")
      parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
      parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
      parser.add_argument("--enable_save_ckpt", type=str, default="true", choices=["true", "false"],
                          help="Enable save checkpoint, default is true.")
      parser.add_argument("--enable_lossscale", type=str, default="true", choices=["true", "false"],
                          help="Use lossscale or not, default is not.")
      parser.add_argument("--do_shuffle", type=str, default="true", choices=["true", "false"],
                          help="Enable shuffle for dataset, default is true.")
      parser.add_argument("--enable_data_sink", type=str, default="true", choices=["true", "false"],
                          help="Enable data sink, default is true.")
      parser.add_argument("--data_sink_steps", type=int, default="1", help="Sink steps for each epoch, default is 1.")
      parser.add_argument("--accumulation_steps", type=int, default="1",
                          help="Accumulating gradients N times before weight update, default is 1.")
      parser.add_argument("--save_checkpoint_path", type=str, default="", help="Save checkpoint path")
      parser.add_argument("--load_checkpoint_path", type=str, default="", help="Load checkpoint file path")
      parser.add_argument("--save_checkpoint_steps", type=int, default=1000, help="Save checkpoint steps, "
                                                                                  "default is 1000.")
      parser.add_argument("--train_steps", type=int, default=-1, help="Training Steps, default is -1, "
                                                                      "meaning run all steps according to epoch number.")
      parser.add_argument("--save_checkpoint_num", type=int, default=1, help="Save checkpoint numbers, default is 1.")
      parser.add_argument("--data_dir", type=str, default="", help="Data path, it is better to use absolute path")
      parser.add_argument("--schema_dir", type=str, default="", help="Schema path, it is better to use absolute path")
      parser.add_argument("--enable_graph_kernel", type=str, default="auto", choices=["auto", "true", "false"],
                          help="Accelerate by graph kernel, default is auto.")
  
      parser.add_argument("--optimizer", type=str, default="AdamWeightDecay", choices=["AdamWeightDecay", "Lamb", "Momentum"],
                          help="Optimizer, default is AdamWeightDecay.")
      parser.add_argument("--enable_global_norm", type=str, default="true", choices=["true", "false"],
                          help="Enable gloabl norm for grad clip, default is true.")
      parser.add_argument("--batch_size", type=int, default=32, help="Batch size, default is 32.")
      parser.add_argument("--dtype", type=str, default="fp32", choices=["fp32", "fp16"],
                          help="dtype, default is fp32.")
  
      args_opt = parser.parse_args()
      cfg.optimizer = args_opt.optimizer
      cfg.batch_size = args_opt.batch_size
      cfg.enable_global_norm = True if args_opt.enable_global_norm == "true" else False
      bert_net_cfg.compute_type = mstype.float32 if args_opt.dtype== "fp32" else mstype.float16
      logger.warning("\nargs_opt: {}".format(args_opt))
      logger.warning("\ncfg: {}".format(cfg))
  
      context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, device_id=args_opt.device_id)
      context.set_context(reserve_class_name_in_scope=False)
      is_auto_enable_graph_kernel = _auto_enable_graph_kernel(args_opt.device_target, args_opt.enable_graph_kernel)
      if args_opt.enable_graph_kernel == "true" or is_auto_enable_graph_kernel:
          context.set_context(enable_graph_kernel=True)
      ckpt_save_dir = args_opt.save_checkpoint_path
      if args_opt.distribute == "true":
          if args_opt.device_target == 'Ascend':
              D.init()
              device_num = args_opt.device_num
              rank = args_opt.device_id % device_num
          else:
              D.init()
              device_num = D.get_group_size()
              rank = D.get_rank()
          ckpt_save_dir = args_opt.save_checkpoint_path + 'ckpt_' + str(get_rank()) + '/'
  
          context.reset_auto_parallel_context()
          context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True,
                                            device_num=device_num)
          _set_bert_all_reduce_split(args_opt.device_target, context.get_context('enable_graph_kernel'))
      else:
          rank = 0
          device_num = 1
  
      if args_opt.accumulation_steps > 1:
          logger.info("accumulation steps: {}".format(args_opt.accumulation_steps))
          logger.info("global batch size: {}".format(cfg.batch_size * args_opt.accumulation_steps))
          if args_opt.enable_data_sink == "true":
              args_opt.data_sink_steps *= args_opt.accumulation_steps
              logger.info("data sink steps: {}".format(args_opt.data_sink_steps))
          if args_opt.enable_save_ckpt == "true":
              args_opt.save_checkpoint_steps *= args_opt.accumulation_steps
              logger.info("save checkpoint steps: {}".format(args_opt.save_checkpoint_steps))
  
      ds = create_bert_dataset(device_num, rank, args_opt.do_shuffle, args_opt.data_dir, args_opt.schema_dir)
      net_with_loss = BertNetworkWithLoss(bert_net_cfg, True)
  
      new_repeat_count = args_opt.epoch_size * ds.get_dataset_size() // args_opt.data_sink_steps
      if args_opt.train_steps > 0:
          train_steps = args_opt.train_steps * args_opt.accumulation_steps
          new_repeat_count = min(new_repeat_count, train_steps // args_opt.data_sink_steps)
      else:
          args_opt.train_steps = args_opt.epoch_size * ds.get_dataset_size() // args_opt.accumulation_steps
          logger.info("train steps: {}".format(args_opt.train_steps))
  
      optimizer = _get_optimizer(args_opt, net_with_loss)
      callback = [TimeMonitor(args_opt.data_sink_steps), LossCallBack(ds.get_dataset_size())]
      if args_opt.enable_save_ckpt == "true" and args_opt.device_id % min(8, device_num) == 0:
          config_ck = CheckpointConfig(save_checkpoint_steps=args_opt.save_checkpoint_steps,
                                       keep_checkpoint_max=args_opt.save_checkpoint_num)
          ckpoint_cb = ModelCheckpoint(prefix='checkpoint_bert',
                                       directory=None if ckpt_save_dir == "" else ckpt_save_dir, config=config_ck)
          callback.append(ckpoint_cb)
  
      if args_opt.load_checkpoint_path:
          param_dict = load_checkpoint(args_opt.load_checkpoint_path)
          load_param_into_net(net_with_loss, param_dict)
  
      if args_opt.enable_lossscale == "true":
          update_cell = DynamicLossScaleUpdateCell(loss_scale_value=cfg.loss_scale_value,
                                                   scale_factor=cfg.scale_factor,
                                                   scale_window=cfg.scale_window)
  
          if args_opt.accumulation_steps <= 1:
              if cfg.optimizer == 'AdamWeightDecay' and args_opt.device_target == 'GPU':
                  net_with_grads = BertTrainOneStepWithLossScaleCellForAdam(net_with_loss, optimizer=optimizer,
                                                                            scale_update_cell=update_cell)
              else:
                  net_with_grads = BertTrainOneStepWithLossScaleCell(net_with_loss, optimizer=optimizer,
                                                                     scale_update_cell=update_cell)
          else:
              accumulation_steps = args_opt.accumulation_steps
              net_with_grads = BertTrainAccumulateStepsWithLossScaleCell(net_with_loss, optimizer=optimizer,
                                                                         scale_update_cell=update_cell,
                                                                         accumulation_steps=accumulation_steps,
                                                                         enable_global_norm=cfg.enable_global_norm)
      else:
          net_with_grads = BertTrainOneStepCell(net_with_loss, optimizer=optimizer)
  
      model = Model(net_with_grads)
      model.train(new_repeat_count, ds, callbacks=callback,
                  dataset_sink_mode=(args_opt.enable_data_sink == "true"), sink_size=args_opt.data_sink_steps)
  
  
  if __name__ == '__main__':
      set_seed(0)
      run_pretrain()
  